Deep learning: a comprehensive guide
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Boca Raton, FL ; Abingdon, Oxon
CRC Press
2022
|
| Edition: | First edition |
| Subjects: | |
| Online Access: | DE-863 DE-862 DE-91 Volltext |
| Physical Description: | 1 Online-Ressource (xv, 290 Seiten) Illustrationen, Diagramme |
| ISBN: | 9781003185635 9781000481884 |
| DOI: | 10.1201/9781003185635 |
Staff View
MARC
| LEADER | 00000nmm a2200000zc 4500 | ||
|---|---|---|---|
| 001 | BV048221476 | ||
| 003 | DE-604 | ||
| 005 | 20241028 | ||
| 007 | cr|uuu---uuuuu | ||
| 008 | 220516s2022 |||| o||u| ||||||eng d | ||
| 020 | |a 9781003185635 |c Online, ebook |9 978-1-003-18563-5 | ||
| 020 | |a 9781000481884 |9 978-1-00-048188-4 | ||
| 024 | 7 | |a 10.1201/9781003185635 |2 doi | |
| 035 | |a (ZDB-30-PQE)EBC6825672 | ||
| 035 | |a (ZDB-30-PAD)EBC6825672 | ||
| 035 | |a (ZDB-89-EBL)EBL6825672 | ||
| 035 | |a (OCoLC)1289367672 | ||
| 035 | |a (DE-599)BVBBV048221476 | ||
| 040 | |a DE-604 |b ger |e rda | ||
| 041 | 0 | |a eng | |
| 049 | |a DE-91 |a DE-863 |a DE-862 | ||
| 082 | 0 | |a 006.3/1 | |
| 084 | |a DAT 708 |2 stub | ||
| 100 | 1 | |a Vasudevan, Shriram K. |e Verfasser |0 (DE-588)1067855165 |4 aut | |
| 245 | 1 | 0 | |a Deep learning |b a comprehensive guide |c Shriram K. Vasudevan, Sini Raj Pulari, Subashri Vasudevan |
| 250 | |a First edition | ||
| 264 | 1 | |a Boca Raton, FL ; Abingdon, Oxon |b CRC Press |c 2022 | |
| 264 | 4 | |c © 2022 | |
| 300 | |a 1 Online-Ressource (xv, 290 Seiten) |b Illustrationen, Diagramme | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b c |2 rdamedia | ||
| 338 | |b cr |2 rdacarrier | ||
| 505 | 8 | |a Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Preface -- The Authors -- Chapter 1 Introduction to Deep Learning -- Learning Objectives -- 1.1 Introduction -- 1.2 The Need: Why Deep Learning? -- 1.3 What Is the Need of a Transition From Machine Learning to Deep Learning? -- 1.4 Deep Learning Applications -- 1.4.1 Self-Driving Cars -- 1.4.2 Emotion Detection -- 1.4.3 Natural Language Processing -- 1.4.4 Entertainment -- 1.4.5 Healthcare -- YouTube Session On Deep Learning Applications -- Key Points to Remember -- Quiz -- Further Reading -- Chapter 2 The Tools and the Prerequisites -- Learning Objectives -- 2.1 Introduction -- 2.2 The Tools -- 2.2.1 Python Libraries - Must Know -- 2.2.2 The Installation Phase -- A. Anaconda Installation -- B. Jupyter Installation -- C. The First Program With the Jupyter -- D. Keras Installation -- 2.3 Datasets - A Quick Glance -- Key Points to Remember -- Quiz -- Chapter 3 Machine Learning: The Fundamentals -- Learning Objectives -- 3.1 Introduction -- 3.2 The Definitions - Yet Another Time -- 3.3 Machine Learning Algorithms -- 3.3.1 Supervised Learning Algorithms -- 3.3.2 The Unsupervised Learning Algorithms -- 3.3.3 Reinforcement Learning -- 3.3.4 Evolutionary Approach -- 3.4 How/Why Do We Need ML? -- 3.5 The ML Framework -- 3.6 Linear Regression - A Complete Understanding -- 3.7 Logistic Regression - A Complete Understanding -- 3.8 Classification - A Must-Know Concept -- 3.8.1 SVM - Support Vector Machines -- 3.8.2 K-NN (K-Nearest Neighbor) -- 3.9 Clustering - An Interesting Concept to Know -- 3.9.1 K-Means Clustering -- Key Points to Remember -- Quiz -- Further Reading -- Chapter 4 The Deep Learning Framework -- Learning Objectives -- 4.1 Introduction -- 4.2 Artificial Neuron -- 4.2.1 Biological Neuron -- 4.2.2 Perceptron -- 4.2.2.1 How a Perceptron Works? | |
| 505 | 8 | |a 4.2.3 Activation Functions -- 4.2.4 Parameters -- 4.2.5 Overfitting -- 4.3 A Few More Terms -- 4.4 Optimizers -- Key Points to Remember -- Quiz -- Further Reading -- Chapter 5 CNN - Convolutional Neural Networks: A Complete Understanding -- Learning Objectives -- 5.1 Introduction -- 5.2 What Is Underfitting, Overfitting and Appropriate Fitting? -- 5.3 Bias/variance - A Quick Learning -- 5.4 Convolutional Neural Networks -- 5.4.1 How Convolution Works -- 5.4.2 How Zero Padding Works -- 5.4.3 How Max Pooling Works -- 5.4.4 The CNN Stack - Architecture -- 5.4.5 What Is the Activation Function? -- 5.4.5.1 Sigmoid Activation Function -- 5.4.5.2 ReLU - Rectified Linear Unit -- 5.4.6 CNN - Model Building - Step By Step -- Key Points to Remember -- Quiz -- Further Reading -- Chapter 6 CNN Architectures: An Evolution -- Learning Objectives -- 6.1 Introduction -- 6.2 LeNET CNN Architecture -- 6.3 VGG16 CNN Architecture -- 6.4 AlexNet CNN Architecture -- 6.5 Other CNN Architectures at a Glance -- Key Points to Remember -- Quiz -- Further Reading -- Chapter 7 Recurrent Neural Networks -- Learning Objectives -- 7.1 Introduction -- 7.2 CNN vs. RNN: A Quick Understanding -- 7.3 RNN vs. Feedforward Neural Networks: A Quick Understanding -- 7.4 Simple RNN -- 7.5 LSTM: Long SHORT-TERM Memory -- 7.6 Gated Recurrent Unit -- Key Points to Remember -- Quiz -- Further Reading -- Chapter 8 Autoencoders -- Learning Objectives -- 8.1 Introduction -- 8.2 What Is an Autoencoder? -- 8.2.1 How Autoencoders Work -- 8.2.2 Properties of Autoencoders -- 8.3 Applications of Autoencoders -- 8.3.1 Data Compression and Dimensionality Reduction -- 8.3.2 Image Denoising -- 8.3.3 Feature Extraction -- 8.3.4 Image Generation -- 8.3.5 Image Colorization -- 8.4 Types of Autoencoders -- 8.4.1 Denoising Autoencoder -- 8.4.2 Vanilla Autoencoder -- 8.4.3 Deep Autoencoder | |
| 505 | 8 | |a 8.4.4 Sparse Autoencoder -- 8.4.5 Undercomplete Autoencoder -- 8.4.6 Stacked Autoencoder -- 8.4.7 Variational Autoencoder (VAEs) -- 8.4.8 Convolutional Autoencoder -- Key Points to Remember -- Quiz -- Further Reading -- Chapter 9 Generative Models -- Learning Objectives -- 9.1 Introduction -- 9.2 What Is a Generative Model? -- 9.3 What Are Generative Adversarial Networks (GAN)? -- 9.4 Types of GAN -- 9.4.1 Deep Convolutional GANs (DCGANs) -- 9.4.2 Stack GAN -- 9.4.3 Cycle GAN -- 9.4.4 Conditional GAN (CGAN) -- 9.4.5 Info GAN -- 9.5 Applications of GAN -- 9.5.1 Fake Image Generation -- 9.5.2 Image Modification -- 9.5.3 Text to Image/Image to Image Generation -- 9.5.4 Speech Modification -- 9.5.5 Assisting Artists -- 9.6 Implementation of GAN -- Key Points to Remember -- Quiz -- Further Reading -- Chapter 10 Transfer Learning -- Learning Objectives -- 10.1 What Is Transfer Learning? -- 10.2 When Can We Use Transfer Learning? -- 10.3 Example - 1: Cat Or Dog Using Transfer Learning With VGG 16 -- 10.4 Example - 2: Identify Your Relatives' Faces Using Transfer Learning -- 10.5 The Difference Between Transfer Learning and Fine Tuning -- 10.6 Transfer Learning Strategies -- 10.6.1 Same Domain, Task -- 10.6.2 Same Domain, Different Task -- 10.6.3 Different Domain, Same Task -- Key Points to Remember -- Quiz -- Further Reading -- Chapter 11 Intel OpenVino: A Must-Know Deep Learning Toolkit -- Learning Objectives -- 11.1 Introduction -- 11.2 OpenVino Installation Guidelines -- Key Points to Remember -- Quiz -- Further Reading -- Chapter 12 Interview Questions and Answers -- Learning Objectives -- YouTube Sessions On Deep Learning Applications -- Index | |
| 650 | 0 | 7 | |a Neuronales Netz |0 (DE-588)4226127-2 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Maschinelles Lernen |0 (DE-588)4193754-5 |2 gnd |9 rswk-swf |
| 689 | 0 | 0 | |a Neuronales Netz |0 (DE-588)4226127-2 |D s |
| 689 | 0 | 1 | |a Maschinelles Lernen |0 (DE-588)4193754-5 |D s |
| 689 | 0 | |5 DE-604 | |
| 700 | 1 | |a Pulari, Sini Raj |e Verfasser |0 (DE-588)125070524X |4 aut | |
| 700 | 1 | |a Vasudevan, Subashri |e Verfasser |0 (DE-588)1250705525 |4 aut | |
| 776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe, Hardcover |z 978-1-032-02882-8 |a Vasudevan, Shriram K. |t Deep Learning |d Milton : CRC Press LLC,c2021 |
| 776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe, Paperback |z 978-1-032-02885-9 |
| 856 | 4 | 0 | |u https://doi.org/10.1201/9781003185635 |x Verlag |z URL des Erstveröffentlichers |3 Volltext |
| 912 | |a ZDB-7-TFC |a ZDB-30-PQE | ||
| 943 | 1 | |a oai:aleph.bib-bvb.de:BVB01-033602213 | |
| 966 | e | |u https://doi.org/10.1201/9781003185635 |l DE-863 |p ZDB-7-TFC |x Verlag |3 Volltext | |
| 966 | e | |u https://doi.org/10.1201/9781003185635 |l DE-862 |p ZDB-7-TFC |x Verlag |3 Volltext | |
| 966 | e | |u https://ebookcentral.proquest.com/lib/munchentech/detail.action?docID=6825672 |l DE-91 |p ZDB-30-PQE |q TUM_PDA_PQE_Kauf |x Aggregator |3 Volltext | |
Record in the Search Index
| _version_ | 1824555895027138562 |
|---|---|
| adam_text | |
| adam_txt | |
| any_adam_object | |
| any_adam_object_boolean | |
| author | Vasudevan, Shriram K. Pulari, Sini Raj Vasudevan, Subashri |
| author_GND | (DE-588)1067855165 (DE-588)125070524X (DE-588)1250705525 |
| author_facet | Vasudevan, Shriram K. Pulari, Sini Raj Vasudevan, Subashri |
| author_role | aut aut aut |
| author_sort | Vasudevan, Shriram K. |
| author_variant | s k v sk skv s r p sr srp s v sv |
| building | Verbundindex |
| bvnumber | BV048221476 |
| classification_tum | DAT 708 |
| collection | ZDB-7-TFC ZDB-30-PQE |
| contents | Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Preface -- The Authors -- Chapter 1 Introduction to Deep Learning -- Learning Objectives -- 1.1 Introduction -- 1.2 The Need: Why Deep Learning? -- 1.3 What Is the Need of a Transition From Machine Learning to Deep Learning? -- 1.4 Deep Learning Applications -- 1.4.1 Self-Driving Cars -- 1.4.2 Emotion Detection -- 1.4.3 Natural Language Processing -- 1.4.4 Entertainment -- 1.4.5 Healthcare -- YouTube Session On Deep Learning Applications -- Key Points to Remember -- Quiz -- Further Reading -- Chapter 2 The Tools and the Prerequisites -- Learning Objectives -- 2.1 Introduction -- 2.2 The Tools -- 2.2.1 Python Libraries - Must Know -- 2.2.2 The Installation Phase -- A. Anaconda Installation -- B. Jupyter Installation -- C. The First Program With the Jupyter -- D. Keras Installation -- 2.3 Datasets - A Quick Glance -- Key Points to Remember -- Quiz -- Chapter 3 Machine Learning: The Fundamentals -- Learning Objectives -- 3.1 Introduction -- 3.2 The Definitions - Yet Another Time -- 3.3 Machine Learning Algorithms -- 3.3.1 Supervised Learning Algorithms -- 3.3.2 The Unsupervised Learning Algorithms -- 3.3.3 Reinforcement Learning -- 3.3.4 Evolutionary Approach -- 3.4 How/Why Do We Need ML? -- 3.5 The ML Framework -- 3.6 Linear Regression - A Complete Understanding -- 3.7 Logistic Regression - A Complete Understanding -- 3.8 Classification - A Must-Know Concept -- 3.8.1 SVM - Support Vector Machines -- 3.8.2 K-NN (K-Nearest Neighbor) -- 3.9 Clustering - An Interesting Concept to Know -- 3.9.1 K-Means Clustering -- Key Points to Remember -- Quiz -- Further Reading -- Chapter 4 The Deep Learning Framework -- Learning Objectives -- 4.1 Introduction -- 4.2 Artificial Neuron -- 4.2.1 Biological Neuron -- 4.2.2 Perceptron -- 4.2.2.1 How a Perceptron Works? 4.2.3 Activation Functions -- 4.2.4 Parameters -- 4.2.5 Overfitting -- 4.3 A Few More Terms -- 4.4 Optimizers -- Key Points to Remember -- Quiz -- Further Reading -- Chapter 5 CNN - Convolutional Neural Networks: A Complete Understanding -- Learning Objectives -- 5.1 Introduction -- 5.2 What Is Underfitting, Overfitting and Appropriate Fitting? -- 5.3 Bias/variance - A Quick Learning -- 5.4 Convolutional Neural Networks -- 5.4.1 How Convolution Works -- 5.4.2 How Zero Padding Works -- 5.4.3 How Max Pooling Works -- 5.4.4 The CNN Stack - Architecture -- 5.4.5 What Is the Activation Function? -- 5.4.5.1 Sigmoid Activation Function -- 5.4.5.2 ReLU - Rectified Linear Unit -- 5.4.6 CNN - Model Building - Step By Step -- Key Points to Remember -- Quiz -- Further Reading -- Chapter 6 CNN Architectures: An Evolution -- Learning Objectives -- 6.1 Introduction -- 6.2 LeNET CNN Architecture -- 6.3 VGG16 CNN Architecture -- 6.4 AlexNet CNN Architecture -- 6.5 Other CNN Architectures at a Glance -- Key Points to Remember -- Quiz -- Further Reading -- Chapter 7 Recurrent Neural Networks -- Learning Objectives -- 7.1 Introduction -- 7.2 CNN vs. RNN: A Quick Understanding -- 7.3 RNN vs. Feedforward Neural Networks: A Quick Understanding -- 7.4 Simple RNN -- 7.5 LSTM: Long SHORT-TERM Memory -- 7.6 Gated Recurrent Unit -- Key Points to Remember -- Quiz -- Further Reading -- Chapter 8 Autoencoders -- Learning Objectives -- 8.1 Introduction -- 8.2 What Is an Autoencoder? -- 8.2.1 How Autoencoders Work -- 8.2.2 Properties of Autoencoders -- 8.3 Applications of Autoencoders -- 8.3.1 Data Compression and Dimensionality Reduction -- 8.3.2 Image Denoising -- 8.3.3 Feature Extraction -- 8.3.4 Image Generation -- 8.3.5 Image Colorization -- 8.4 Types of Autoencoders -- 8.4.1 Denoising Autoencoder -- 8.4.2 Vanilla Autoencoder -- 8.4.3 Deep Autoencoder 8.4.4 Sparse Autoencoder -- 8.4.5 Undercomplete Autoencoder -- 8.4.6 Stacked Autoencoder -- 8.4.7 Variational Autoencoder (VAEs) -- 8.4.8 Convolutional Autoencoder -- Key Points to Remember -- Quiz -- Further Reading -- Chapter 9 Generative Models -- Learning Objectives -- 9.1 Introduction -- 9.2 What Is a Generative Model? -- 9.3 What Are Generative Adversarial Networks (GAN)? -- 9.4 Types of GAN -- 9.4.1 Deep Convolutional GANs (DCGANs) -- 9.4.2 Stack GAN -- 9.4.3 Cycle GAN -- 9.4.4 Conditional GAN (CGAN) -- 9.4.5 Info GAN -- 9.5 Applications of GAN -- 9.5.1 Fake Image Generation -- 9.5.2 Image Modification -- 9.5.3 Text to Image/Image to Image Generation -- 9.5.4 Speech Modification -- 9.5.5 Assisting Artists -- 9.6 Implementation of GAN -- Key Points to Remember -- Quiz -- Further Reading -- Chapter 10 Transfer Learning -- Learning Objectives -- 10.1 What Is Transfer Learning? -- 10.2 When Can We Use Transfer Learning? -- 10.3 Example - 1: Cat Or Dog Using Transfer Learning With VGG 16 -- 10.4 Example - 2: Identify Your Relatives' Faces Using Transfer Learning -- 10.5 The Difference Between Transfer Learning and Fine Tuning -- 10.6 Transfer Learning Strategies -- 10.6.1 Same Domain, Task -- 10.6.2 Same Domain, Different Task -- 10.6.3 Different Domain, Same Task -- Key Points to Remember -- Quiz -- Further Reading -- Chapter 11 Intel OpenVino: A Must-Know Deep Learning Toolkit -- Learning Objectives -- 11.1 Introduction -- 11.2 OpenVino Installation Guidelines -- Key Points to Remember -- Quiz -- Further Reading -- Chapter 12 Interview Questions and Answers -- Learning Objectives -- YouTube Sessions On Deep Learning Applications -- Index |
| ctrlnum | (ZDB-30-PQE)EBC6825672 (ZDB-30-PAD)EBC6825672 (ZDB-89-EBL)EBL6825672 (OCoLC)1289367672 (DE-599)BVBBV048221476 |
| dewey-full | 006.3/1 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.3/1 |
| dewey-search | 006.3/1 |
| dewey-sort | 16.3 11 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| discipline_str_mv | Informatik |
| doi_str_mv | 10.1201/9781003185635 |
| edition | First edition |
| format | Electronic eBook |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>00000nmm a2200000zc 4500</leader><controlfield tag="001">BV048221476</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">20241028</controlfield><controlfield tag="007">cr|uuu---uuuuu</controlfield><controlfield tag="008">220516s2022 |||| o||u| ||||||eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781003185635</subfield><subfield code="c">Online, ebook</subfield><subfield code="9">978-1-003-18563-5</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781000481884</subfield><subfield code="9">978-1-00-048188-4</subfield></datafield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1201/9781003185635</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ZDB-30-PQE)EBC6825672</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ZDB-30-PAD)EBC6825672</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ZDB-89-EBL)EBL6825672</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1289367672</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV048221476</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-91</subfield><subfield code="a">DE-863</subfield><subfield code="a">DE-862</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">006.3/1</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">DAT 708</subfield><subfield code="2">stub</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Vasudevan, Shriram K.</subfield><subfield code="e">Verfasser</subfield><subfield code="0">(DE-588)1067855165</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Deep learning</subfield><subfield code="b">a comprehensive guide</subfield><subfield code="c">Shriram K. Vasudevan, Sini Raj Pulari, Subashri Vasudevan</subfield></datafield><datafield tag="250" ind1=" " ind2=" "><subfield code="a">First edition</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Boca Raton, FL ; Abingdon, Oxon</subfield><subfield code="b">CRC Press</subfield><subfield code="c">2022</subfield></datafield><datafield tag="264" ind1=" " ind2="4"><subfield code="c">© 2022</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (xv, 290 Seiten)</subfield><subfield code="b">Illustrationen, Diagramme</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Preface -- The Authors -- Chapter 1 Introduction to Deep Learning -- Learning Objectives -- 1.1 Introduction -- 1.2 The Need: Why Deep Learning? -- 1.3 What Is the Need of a Transition From Machine Learning to Deep Learning? -- 1.4 Deep Learning Applications -- 1.4.1 Self-Driving Cars -- 1.4.2 Emotion Detection -- 1.4.3 Natural Language Processing -- 1.4.4 Entertainment -- 1.4.5 Healthcare -- YouTube Session On Deep Learning Applications -- Key Points to Remember -- Quiz -- Further Reading -- Chapter 2 The Tools and the Prerequisites -- Learning Objectives -- 2.1 Introduction -- 2.2 The Tools -- 2.2.1 Python Libraries - Must Know -- 2.2.2 The Installation Phase -- A. Anaconda Installation -- B. Jupyter Installation -- C. The First Program With the Jupyter -- D. Keras Installation -- 2.3 Datasets - A Quick Glance -- Key Points to Remember -- Quiz -- Chapter 3 Machine Learning: The Fundamentals -- Learning Objectives -- 3.1 Introduction -- 3.2 The Definitions - Yet Another Time -- 3.3 Machine Learning Algorithms -- 3.3.1 Supervised Learning Algorithms -- 3.3.2 The Unsupervised Learning Algorithms -- 3.3.3 Reinforcement Learning -- 3.3.4 Evolutionary Approach -- 3.4 How/Why Do We Need ML? -- 3.5 The ML Framework -- 3.6 Linear Regression - A Complete Understanding -- 3.7 Logistic Regression - A Complete Understanding -- 3.8 Classification - A Must-Know Concept -- 3.8.1 SVM - Support Vector Machines -- 3.8.2 K-NN (K-Nearest Neighbor) -- 3.9 Clustering - An Interesting Concept to Know -- 3.9.1 K-Means Clustering -- Key Points to Remember -- Quiz -- Further Reading -- Chapter 4 The Deep Learning Framework -- Learning Objectives -- 4.1 Introduction -- 4.2 Artificial Neuron -- 4.2.1 Biological Neuron -- 4.2.2 Perceptron -- 4.2.2.1 How a Perceptron Works?</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">4.2.3 Activation Functions -- 4.2.4 Parameters -- 4.2.5 Overfitting -- 4.3 A Few More Terms -- 4.4 Optimizers -- Key Points to Remember -- Quiz -- Further Reading -- Chapter 5 CNN - Convolutional Neural Networks: A Complete Understanding -- Learning Objectives -- 5.1 Introduction -- 5.2 What Is Underfitting, Overfitting and Appropriate Fitting? -- 5.3 Bias/variance - A Quick Learning -- 5.4 Convolutional Neural Networks -- 5.4.1 How Convolution Works -- 5.4.2 How Zero Padding Works -- 5.4.3 How Max Pooling Works -- 5.4.4 The CNN Stack - Architecture -- 5.4.5 What Is the Activation Function? -- 5.4.5.1 Sigmoid Activation Function -- 5.4.5.2 ReLU - Rectified Linear Unit -- 5.4.6 CNN - Model Building - Step By Step -- Key Points to Remember -- Quiz -- Further Reading -- Chapter 6 CNN Architectures: An Evolution -- Learning Objectives -- 6.1 Introduction -- 6.2 LeNET CNN Architecture -- 6.3 VGG16 CNN Architecture -- 6.4 AlexNet CNN Architecture -- 6.5 Other CNN Architectures at a Glance -- Key Points to Remember -- Quiz -- Further Reading -- Chapter 7 Recurrent Neural Networks -- Learning Objectives -- 7.1 Introduction -- 7.2 CNN vs. RNN: A Quick Understanding -- 7.3 RNN vs. Feedforward Neural Networks: A Quick Understanding -- 7.4 Simple RNN -- 7.5 LSTM: Long SHORT-TERM Memory -- 7.6 Gated Recurrent Unit -- Key Points to Remember -- Quiz -- Further Reading -- Chapter 8 Autoencoders -- Learning Objectives -- 8.1 Introduction -- 8.2 What Is an Autoencoder? -- 8.2.1 How Autoencoders Work -- 8.2.2 Properties of Autoencoders -- 8.3 Applications of Autoencoders -- 8.3.1 Data Compression and Dimensionality Reduction -- 8.3.2 Image Denoising -- 8.3.3 Feature Extraction -- 8.3.4 Image Generation -- 8.3.5 Image Colorization -- 8.4 Types of Autoencoders -- 8.4.1 Denoising Autoencoder -- 8.4.2 Vanilla Autoencoder -- 8.4.3 Deep Autoencoder</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">8.4.4 Sparse Autoencoder -- 8.4.5 Undercomplete Autoencoder -- 8.4.6 Stacked Autoencoder -- 8.4.7 Variational Autoencoder (VAEs) -- 8.4.8 Convolutional Autoencoder -- Key Points to Remember -- Quiz -- Further Reading -- Chapter 9 Generative Models -- Learning Objectives -- 9.1 Introduction -- 9.2 What Is a Generative Model? -- 9.3 What Are Generative Adversarial Networks (GAN)? -- 9.4 Types of GAN -- 9.4.1 Deep Convolutional GANs (DCGANs) -- 9.4.2 Stack GAN -- 9.4.3 Cycle GAN -- 9.4.4 Conditional GAN (CGAN) -- 9.4.5 Info GAN -- 9.5 Applications of GAN -- 9.5.1 Fake Image Generation -- 9.5.2 Image Modification -- 9.5.3 Text to Image/Image to Image Generation -- 9.5.4 Speech Modification -- 9.5.5 Assisting Artists -- 9.6 Implementation of GAN -- Key Points to Remember -- Quiz -- Further Reading -- Chapter 10 Transfer Learning -- Learning Objectives -- 10.1 What Is Transfer Learning? -- 10.2 When Can We Use Transfer Learning? -- 10.3 Example - 1: Cat Or Dog Using Transfer Learning With VGG 16 -- 10.4 Example - 2: Identify Your Relatives' Faces Using Transfer Learning -- 10.5 The Difference Between Transfer Learning and Fine Tuning -- 10.6 Transfer Learning Strategies -- 10.6.1 Same Domain, Task -- 10.6.2 Same Domain, Different Task -- 10.6.3 Different Domain, Same Task -- Key Points to Remember -- Quiz -- Further Reading -- Chapter 11 Intel OpenVino: A Must-Know Deep Learning Toolkit -- Learning Objectives -- 11.1 Introduction -- 11.2 OpenVino Installation Guidelines -- Key Points to Remember -- Quiz -- Further Reading -- Chapter 12 Interview Questions and Answers -- Learning Objectives -- YouTube Sessions On Deep Learning Applications -- Index</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Neuronales Netz</subfield><subfield code="0">(DE-588)4226127-2</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Maschinelles Lernen</subfield><subfield code="0">(DE-588)4193754-5</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="689" ind1="0" ind2="0"><subfield code="a">Neuronales Netz</subfield><subfield code="0">(DE-588)4226127-2</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="1"><subfield code="a">Maschinelles Lernen</subfield><subfield code="0">(DE-588)4193754-5</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2=" "><subfield code="5">DE-604</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Pulari, Sini Raj</subfield><subfield code="e">Verfasser</subfield><subfield code="0">(DE-588)125070524X</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Vasudevan, Subashri</subfield><subfield code="e">Verfasser</subfield><subfield code="0">(DE-588)1250705525</subfield><subfield code="4">aut</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Druck-Ausgabe, Hardcover</subfield><subfield code="z">978-1-032-02882-8</subfield><subfield code="a">Vasudevan, Shriram K.</subfield><subfield code="t">Deep Learning</subfield><subfield code="d">Milton : CRC Press LLC,c2021</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Druck-Ausgabe, Paperback</subfield><subfield code="z">978-1-032-02885-9</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1201/9781003185635</subfield><subfield code="x">Verlag</subfield><subfield code="z">URL des Erstveröffentlichers</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-7-TFC</subfield><subfield code="a">ZDB-30-PQE</subfield></datafield><datafield tag="943" ind1="1" ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-033602213</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1201/9781003185635</subfield><subfield code="l">DE-863</subfield><subfield code="p">ZDB-7-TFC</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1201/9781003185635</subfield><subfield code="l">DE-862</subfield><subfield code="p">ZDB-7-TFC</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://ebookcentral.proquest.com/lib/munchentech/detail.action?docID=6825672</subfield><subfield code="l">DE-91</subfield><subfield code="p">ZDB-30-PQE</subfield><subfield code="q">TUM_PDA_PQE_Kauf</subfield><subfield code="x">Aggregator</subfield><subfield code="3">Volltext</subfield></datafield></record></collection> |
| id | DE-604.BV048221476 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T19:50:32Z |
| indexdate | 2025-02-20T07:14:55Z |
| institution | BVB |
| isbn | 9781003185635 9781000481884 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-033602213 |
| oclc_num | 1289367672 |
| open_access_boolean | |
| owner | DE-91 DE-BY-TUM DE-863 DE-BY-FWS DE-862 DE-BY-FWS |
| owner_facet | DE-91 DE-BY-TUM DE-863 DE-BY-FWS DE-862 DE-BY-FWS |
| physical | 1 Online-Ressource (xv, 290 Seiten) Illustrationen, Diagramme |
| psigel | ZDB-7-TFC ZDB-30-PQE ZDB-30-PQE TUM_PDA_PQE_Kauf |
| publishDate | 2022 |
| publishDateSearch | 2022 |
| publishDateSort | 2022 |
| publisher | CRC Press |
| record_format | marc |
| spellingShingle | Vasudevan, Shriram K. Pulari, Sini Raj Vasudevan, Subashri Deep learning a comprehensive guide Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Preface -- The Authors -- Chapter 1 Introduction to Deep Learning -- Learning Objectives -- 1.1 Introduction -- 1.2 The Need: Why Deep Learning? -- 1.3 What Is the Need of a Transition From Machine Learning to Deep Learning? -- 1.4 Deep Learning Applications -- 1.4.1 Self-Driving Cars -- 1.4.2 Emotion Detection -- 1.4.3 Natural Language Processing -- 1.4.4 Entertainment -- 1.4.5 Healthcare -- YouTube Session On Deep Learning Applications -- Key Points to Remember -- Quiz -- Further Reading -- Chapter 2 The Tools and the Prerequisites -- Learning Objectives -- 2.1 Introduction -- 2.2 The Tools -- 2.2.1 Python Libraries - Must Know -- 2.2.2 The Installation Phase -- A. Anaconda Installation -- B. Jupyter Installation -- C. The First Program With the Jupyter -- D. Keras Installation -- 2.3 Datasets - A Quick Glance -- Key Points to Remember -- Quiz -- Chapter 3 Machine Learning: The Fundamentals -- Learning Objectives -- 3.1 Introduction -- 3.2 The Definitions - Yet Another Time -- 3.3 Machine Learning Algorithms -- 3.3.1 Supervised Learning Algorithms -- 3.3.2 The Unsupervised Learning Algorithms -- 3.3.3 Reinforcement Learning -- 3.3.4 Evolutionary Approach -- 3.4 How/Why Do We Need ML? -- 3.5 The ML Framework -- 3.6 Linear Regression - A Complete Understanding -- 3.7 Logistic Regression - A Complete Understanding -- 3.8 Classification - A Must-Know Concept -- 3.8.1 SVM - Support Vector Machines -- 3.8.2 K-NN (K-Nearest Neighbor) -- 3.9 Clustering - An Interesting Concept to Know -- 3.9.1 K-Means Clustering -- Key Points to Remember -- Quiz -- Further Reading -- Chapter 4 The Deep Learning Framework -- Learning Objectives -- 4.1 Introduction -- 4.2 Artificial Neuron -- 4.2.1 Biological Neuron -- 4.2.2 Perceptron -- 4.2.2.1 How a Perceptron Works? 4.2.3 Activation Functions -- 4.2.4 Parameters -- 4.2.5 Overfitting -- 4.3 A Few More Terms -- 4.4 Optimizers -- Key Points to Remember -- Quiz -- Further Reading -- Chapter 5 CNN - Convolutional Neural Networks: A Complete Understanding -- Learning Objectives -- 5.1 Introduction -- 5.2 What Is Underfitting, Overfitting and Appropriate Fitting? -- 5.3 Bias/variance - A Quick Learning -- 5.4 Convolutional Neural Networks -- 5.4.1 How Convolution Works -- 5.4.2 How Zero Padding Works -- 5.4.3 How Max Pooling Works -- 5.4.4 The CNN Stack - Architecture -- 5.4.5 What Is the Activation Function? -- 5.4.5.1 Sigmoid Activation Function -- 5.4.5.2 ReLU - Rectified Linear Unit -- 5.4.6 CNN - Model Building - Step By Step -- Key Points to Remember -- Quiz -- Further Reading -- Chapter 6 CNN Architectures: An Evolution -- Learning Objectives -- 6.1 Introduction -- 6.2 LeNET CNN Architecture -- 6.3 VGG16 CNN Architecture -- 6.4 AlexNet CNN Architecture -- 6.5 Other CNN Architectures at a Glance -- Key Points to Remember -- Quiz -- Further Reading -- Chapter 7 Recurrent Neural Networks -- Learning Objectives -- 7.1 Introduction -- 7.2 CNN vs. RNN: A Quick Understanding -- 7.3 RNN vs. Feedforward Neural Networks: A Quick Understanding -- 7.4 Simple RNN -- 7.5 LSTM: Long SHORT-TERM Memory -- 7.6 Gated Recurrent Unit -- Key Points to Remember -- Quiz -- Further Reading -- Chapter 8 Autoencoders -- Learning Objectives -- 8.1 Introduction -- 8.2 What Is an Autoencoder? -- 8.2.1 How Autoencoders Work -- 8.2.2 Properties of Autoencoders -- 8.3 Applications of Autoencoders -- 8.3.1 Data Compression and Dimensionality Reduction -- 8.3.2 Image Denoising -- 8.3.3 Feature Extraction -- 8.3.4 Image Generation -- 8.3.5 Image Colorization -- 8.4 Types of Autoencoders -- 8.4.1 Denoising Autoencoder -- 8.4.2 Vanilla Autoencoder -- 8.4.3 Deep Autoencoder 8.4.4 Sparse Autoencoder -- 8.4.5 Undercomplete Autoencoder -- 8.4.6 Stacked Autoencoder -- 8.4.7 Variational Autoencoder (VAEs) -- 8.4.8 Convolutional Autoencoder -- Key Points to Remember -- Quiz -- Further Reading -- Chapter 9 Generative Models -- Learning Objectives -- 9.1 Introduction -- 9.2 What Is a Generative Model? -- 9.3 What Are Generative Adversarial Networks (GAN)? -- 9.4 Types of GAN -- 9.4.1 Deep Convolutional GANs (DCGANs) -- 9.4.2 Stack GAN -- 9.4.3 Cycle GAN -- 9.4.4 Conditional GAN (CGAN) -- 9.4.5 Info GAN -- 9.5 Applications of GAN -- 9.5.1 Fake Image Generation -- 9.5.2 Image Modification -- 9.5.3 Text to Image/Image to Image Generation -- 9.5.4 Speech Modification -- 9.5.5 Assisting Artists -- 9.6 Implementation of GAN -- Key Points to Remember -- Quiz -- Further Reading -- Chapter 10 Transfer Learning -- Learning Objectives -- 10.1 What Is Transfer Learning? -- 10.2 When Can We Use Transfer Learning? -- 10.3 Example - 1: Cat Or Dog Using Transfer Learning With VGG 16 -- 10.4 Example - 2: Identify Your Relatives' Faces Using Transfer Learning -- 10.5 The Difference Between Transfer Learning and Fine Tuning -- 10.6 Transfer Learning Strategies -- 10.6.1 Same Domain, Task -- 10.6.2 Same Domain, Different Task -- 10.6.3 Different Domain, Same Task -- Key Points to Remember -- Quiz -- Further Reading -- Chapter 11 Intel OpenVino: A Must-Know Deep Learning Toolkit -- Learning Objectives -- 11.1 Introduction -- 11.2 OpenVino Installation Guidelines -- Key Points to Remember -- Quiz -- Further Reading -- Chapter 12 Interview Questions and Answers -- Learning Objectives -- YouTube Sessions On Deep Learning Applications -- Index Neuronales Netz (DE-588)4226127-2 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4226127-2 (DE-588)4193754-5 |
| title | Deep learning a comprehensive guide |
| title_auth | Deep learning a comprehensive guide |
| title_exact_search | Deep learning a comprehensive guide |
| title_exact_search_txtP | Deep learning a comprehensive guide |
| title_full | Deep learning a comprehensive guide Shriram K. Vasudevan, Sini Raj Pulari, Subashri Vasudevan |
| title_fullStr | Deep learning a comprehensive guide Shriram K. Vasudevan, Sini Raj Pulari, Subashri Vasudevan |
| title_full_unstemmed | Deep learning a comprehensive guide Shriram K. Vasudevan, Sini Raj Pulari, Subashri Vasudevan |
| title_short | Deep learning |
| title_sort | deep learning a comprehensive guide |
| title_sub | a comprehensive guide |
| topic | Neuronales Netz (DE-588)4226127-2 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Neuronales Netz Maschinelles Lernen |
| url | https://doi.org/10.1201/9781003185635 |
| work_keys_str_mv | AT vasudevanshriramk deeplearningacomprehensiveguide AT pularisiniraj deeplearningacomprehensiveguide AT vasudevansubashri deeplearningacomprehensiveguide |